Federated learning (FL) has emerged as a pivotal paradigm for distributed model training in edge computing (EC), enabling cooperation among numerous Internet of Things devices while safeguarding their data privacy. Despite its successes in machine learning, concerns regarding data security and model fidelity necessitate the efficient unlearning of target device, i.e., federated unlearning (FUN). However, due to resource constraints, device heterogeneity, and non-independent and identically distributed (Non-IID) data, securely eliminating a device’s impact without retraining the model from scratch presents a complex challenge. In response to these challenges, we propose a hierarchical FUN framework, called Hier-FUN. Hier-FUN organizes edge devices into K clusters, each managed by a head device responsible for aggregating local models within the cluster. To expedite both the learning and unlearning processes of Hier-FUN, we design a heuristic algorithm to determine an appropriate value for K based on devices’ data distributions and available resources. In addition, Hier-FUN denies the communication between the server and cluster heads during training, which can constrain the influence sphere of target device and accelerate the unlearning process. We conduct extensive experiments using real-world datasets, and the experimental results illustrate that Hier-FUN can improve test accuracy by 3.19% during the learning phase and achieve a$6.8\times $speedup during unlearning compared with the baseline methods.

Motivated by the need to reduce vendor lock-in and address concerns regarding dedicated hardware availability, cloud applications have increasingly adopted a multi-cloud deployment strategy, in which cloud applications are deployed in different zones associated with various cloud service providers. When deploying cloud applications in multi-clouds, there are three crucial and coupled metrics:deployment cost,access delayandtraffic demand. Unfortunately, existing works overlook either the data transfer cost in deployment cost or the access delay and traffic demand requirements, resulting in high operating costs or low user QoS. To bridge this gap, this paper proposes theCost-EfficientApplicationDeployment Framework (CADER) with tenant requirement guarantee in multi-clouds environment. However, due to the challenges of service price heterogeneity, transfer cost diversity, and resource limitation, achieving cost-efficient cloud application deployment while satisfying all tenant requirements is not an easy task. To tackle this issue, we design an approximate algorithm based on the random rounding method and prove that its approximate ratio is$O(\log g)$, where$g$is the number of cloud zones. Results of in-depth simulations indicate that CADER can reduce the application deployment cost ranging from 16% to 38% compared to commonly used alternatives while ensuring the satisfaction of tenant requirements.

Service function chain (SFC) consists of multiple ordered network functions (e.g., firewall, load balancer) and plays an important role in improving network security and ensuring network performance. Offloading SFCs onto programmable switches can bring significant performance improvement, but it suffers from unbearable reconfiguration delays, making it hard to cope with network workload dynamics in a timely manner. To bridge the gap, this paper presents OptRec, an efficient SFC proactive reconfiguration optimization framework based on deep reinforcement learning (DRL). OptRec predicts future traffic and places SFCs on programmable switches in advance to ensure the timeliness of the SFC reconfiguration, which is a proactive approach. However, it is non-trivial to extract effective features from historical traffic information and global network states, while ensuring efficient and stable model training. To this end, OptRec introduces a multi-level feature extraction model for different types of features. Additionally, it combines reinforcement learning and autoregressive learning to enhance model efficiency and stability. Results of in-depth simulations based on real-world datasets show the average prediction error of OptRec is less than 3 can increase the system throughput by up to 69.6 compared with other alternatives.

Service function chain (SFC) consists of multiple ordered network functions (e.g., firewall, load balancer) and plays an important role in improving network security and ensuring network performance. Offloading SFCs onto programmable switches can bring significant performance improvement, but it suffers from unbearable reconfiguration delays, making it hard to cope with network workload dynamics in a timely manner. To bridge the gap, this paper presents OptRec, an efficient SFC reconfiguration optimization framework based on deep reinforcement learning (DRL). OptRec predicts future traffic and places SFCs on programmable switches in advance to ensure the timeliness of the SFC reconfiguration, which is a proactive approach. However, it is non-trivial to extract effective features from historical traffic information and ensure efficient and stable model training. To this end, OptRec introduces a multi-level feature extraction model for different types of features. Additionally, it combines reinforcement learning and autoregressive learning to enhance model efficiency and stability. Results of in-depth simulations based on real-world datasets show the average prediction error of OptRec is less than 3% and OptRec can increase the system throughput by up to 69.6%~72.6% compared with other alternatives.

As the parameter scale of large-scale models continues to increase, distributed model training imposes significant communication overhead in data centers, resulting in reduced training efficiency. To address this challenge, a promising solution, called in-network aggregation, is proposed to mitigate the bandwidth bottleneck in data centers by aggregating gradients within network. However, existing works mainly rely on programmable switches to implement in-network aggregation, while programmable switches have limited memory resources, which makes it hard to storage parameter with large size. Moreover, programmable switches are not yet widely deployed in current data centers, resulting in poor availability. To overcome this limitation, we leverage servers with idle resources in data centers for in-network aggregation, since servers have more powerful storage capabilities compared with programmable switches. Specifically, we formally formulate the problem of server-based in-network aggregation. An efficient approximate algorithm with bounded approximation factor is proposed to select servers with idle resources and paths for model aggregation. Our extensive simulations show that our proposed method can reduce communication time by 38.4%-60.1% compared to state-of-the-art solutions.

Cloud computing plays an increasingly vital role in both commercial and personal services. In multi-tenant clouds, cloud providers encounter challenges such as physical machine failures and malicious tenant attacks. Ensuring the reliability and robustness of cloud remain significant and complex challenges for cloud providers to improve quality of service and profitability. Previous works either fail to strike a balance between these aspects or result in resource waste and increased costs. In this paper, we propose an innovative virtual machine placement solution without additional resource overhead. Specifically, we place tenants’ virtual machines (VMs) on physical machines (PMs) that meet the reliability requirements specified in the service level agreement, while also limiting the number of PMs to mitigate the impact of malicious attacks, thereby enhancing the robustness of the cloud. However, the dynamic nature of tenant traffic exacerbates the complexity of the problem. To tackle this challenge, we present KR-OPD, a two-stage algorithm with superior competitive ratios. Through large-scale simulations and small-scale testbed, KR-OPD outperforms existing state-of-the-art solutions. For example, our algorithm reduces the affected range of tenants by 47%-64% and the packet loss rate of PM nodes by over 70% compared with other alternatives.

In a hyper-scale cloud data center, a large number of control messages are distributed to hundreds of thousands of compute nodes from a logically centralized control plane. The delivery of these control messages, a.k.a. southbound messages, is critical to cloud infrastructure management, as it greatly affects customer experience. Existing works mainly deal with southbound message delivery by two methods: point-to-point transmission and Message Queue (MQ)-based solutions. With the point-to-point transmission method, each message is sent from the control plane to compute nodes directly, which may cause high control complexity and overhead in hyper-scale clouds. The MQ-based method can address the challenge of high complexity through message aggregation and subscribe/publish model. However, it usually brings in redundant messages, and further causes extra load on compute nodes. To solve the problem above, we design SNIP, which exploits the ability of programmable switches to perform in-network message pruning and to reduce message redundancy. Specffically, forwarding and processing information computed by the control plane is attached to the package header of every control message. Redundant messages can be identified and processed by programmable switches. In addition, we propose a rounding-based algorithm to prune messages with minimal redundancy. The simulation results show that SNIP can reduce the control overhead by 80%-85% and the total traffic of redundant messages by 35% compared with existing solutions.

In Mobile Edge Computing (MEC), users' traffic needs to traverse a set of service functions in a specific order, referred to as a service function chain (SFC), to complete service requests. Thus, SFC routing is an essential issue in MEC. In practice, user mobility and resource limitation are two critical challenges of SFC routing in MEC. However, the previous works either ignore the user mobility or resource limitation, especially the flow-table resources, leading to high transmission latency and resource overhead. In this paper, we study the mobility-aware service function chain routing in MEC. We design an SFC routing scheme called MASCOT to address the above challenges. MASCOT implements SFC routing through three steps: user location prediction, routing path decision, and packet forwarding. For user location prediction, we adopt the order-K Markov prediction method to predict users' next accessed base station. For routing path decision, we formulate the SFC routing selection (SRS) problem, which respects the resource constraints. We propose a primal-dual online SFC routing algorithm (POSR) for the SRS problem and prove that POSR can achieve good competitiveness. For packet forwarding, we propose a forwarding scheme based on segment routing to address the resource limitation challenge further. Extensive simulation results show that our scheme can improve the system throughput by about 40% compared with the state-of-the-art approaches.

In multi-tenant clouds, requests need to traverse a set of network functions (NFs) in a specific order, referred to as a service function chain (SFC), for security and business logic issues. Due to workload dynamics, the central controller of a multi-tenant cloud needs to frequently update the SFC routing, so as to optimize various network performance, such as load balancing. To achieve effective SFC routing update, we should consider two critical requirements:system robustnessandreal-time update. Without considering these two requirements, prior works either result in fragile clouds or suffer from large update delay. In this paper, we propose a robustness-aware real-time SFC routing update (R3-UA) scheme which takes both requirements into consideration. R3-UA pursues robustness-aware real-time routing update through two phases: robust NF instance assignment update and real-time SFC routing update. Two algorithms with bounded approximation ratios are proposed for these two phases, respectively. We implement R3-UA on a real testbed. Both small-scale experimental results and large-scale simulation results show the superior performance of R3-UA compared with other alternatives.

Cloud providers host various services for tenants’ requests (e.g., software-as-a-service) and seek to serve as many requests as possible for revenue maximization. Considering a large number of requests, the previous works on fine-grained request scheduling may lead to poor system scalability (or high schedule overhead) and break tenant isolation. In this article, we design a tenant-grained request scheduling framework to conquer the above two disadvantages. We formulate the tenant-grained request scheduling problem as an integer linear programming and prove its NP-hardness. We consider two complementary cases: the offline case (where we know all request demands in advance), and the online case (where we have to make immediate scheduling decisions for requests arriving online). A normalization-based algorithm with an approximation factor of$ {O}(1)$is proposed to solve the offline problem and a primal-dual-based algorithm with a competitive ratio of$[(1-\epsilon), {O}(\log 3\cdot n+\log (1/\epsilon))]$is designed for the online scenario, where$\epsilon \in (0,1)$and$n$is the number of racks in the cloud. We also discuss how to integrate our proposed algorithms with the previous (fine-grained) request scheduling mechanism. Extensive simulation and experiment results show that our algorithms can obtain significant performance gains, e.g., the online algorithm reduces the scheduler's overhead more than$90\%$and achieves tenant isolation, while obtaining similar network performance (e.g., throughput) compared with the fine-grained request scheduling methods.

In multi-tenant clouds, requests need to traverse a set of network functions (NFs) in a specific order, referred to as a service function chain (SFC), for security and business logic issues. Due to workload dynamics, the central controller of a multi-tenant cloud needs to frequently update the SFC routing, so as to optimize various network performance, such as load balancing. To achieve effective SFC routing update, we should consider two critical requirements: system robustness and real-time update. Without considering these two requirements, prior works either result in fragile clouds or suffer from large update delay. In this paper, we propose a robustness-aware real-time SFC routing update (R3-UA) scheme which takes both requirements into consideration. R3-UA pursues robustness-aware real-time routing update through two phases: robust NF instance assignment and real-time SFC routing update. Two algorithms with bounded approximation ratios are proposed for these two phases, respectively. The large-scale simulation results show the superior performance of R3-UA compared with other alternatives.